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» Retrieving Objects Using Local Integral Invariants
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IJCV
2008
266views more  IJCV 2008»
13 years 9 months ago
Learning to Recognize Objects with Little Supervision
This paper shows (i) improvements over state-of-the-art local feature recognition systems, (ii) how to formulate principled models for automatic local feature selection in object c...
Peter Carbonetto, Gyuri Dorkó, Cordelia Sch...
SPIESR
2000
104views Database» more  SPIESR 2000»
13 years 10 months ago
Feature localization and search by object model under illumination change
Color object recognition methods that are based on image retrieval algorithms can handle changes of illumination via image normalization, e.g. simple color-channel-normalization1 ...
Mark S. Drew, Zinovi Tauber, Ze-Nian Li
ICPR
2004
IEEE
14 years 10 months ago
Multiscale Fourier Descriptor for Shape-Based Image Retrieval
The shapes occurring in the images are important in the content-based image retrieval. In this paper we introduce a new Fourier-based descriptor for the characterization of the sh...
Iivari Kunttu, Leena Lepistö, Juhani Rauhamaa...
CVPR
2008
IEEE
14 years 11 months ago
Beyond sliding windows: Object localization by efficient subwindow search
Most successful object recognition systems rely on binary classification, deciding only if an object is present or not, but not providing information on the actual object location...
Christoph H. Lampert, Matthew B. Blaschko, Thomas ...
ISVC
2010
Springer
13 years 7 months ago
How to Overcome Perceptual Aliasing in ASIFT?
SIFT is one of the most popular algorithms to extract points of interest from images. It is a scale+rotation invariant method. As a consequence, if one compares points of interest ...
Nicolas Noury, Frédéric Sur, Marie-O...